Beyond Comon's Identifiability Theorem for Independent Component Analysis
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Blind sparse source separation using cluster particle swarm optimization technique
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Single channel audio source separation
WSEAS Transactions on Signal Processing
A maximum likelihood approach to nonlinear convolutive blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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We present an iterative inversion (II) approach to blind source separation (BSS). It consists of a quasi-Newton method for the resolution of an estimating equation obtained from the implicit inversion of a robust estimate of the mixing system. The resulting learning rule includes several existing algorithms for BSS as particular cases giving them a novel and unified interpretation. It also provides a justification of the Cardoso and Laheld (1996) step size normalization. The II method is first presented for instantaneous mixtures and then extended to the problem of blind separation of convolutive mixtures. Finally, we derive the necessary and sufficient asymptotic stability conditions for both the instantaneous and convolutive methods to converge.